In [1]:
import numpy as np
import cv2
import glob
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
from os import path
filename=[]
target_corners='output_images/corners/corners_'
target_undist='output_images/undistorted/undist_'
target_pipeline_comb='output_images/pipeline/comb_'
target_warped='output_images/warped/warped_'
target_perspective='output_images/warped/perspective_'
In [3]:
def cam_cali(objpoints, imgpoints):
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
return ret, mtx, dist, rvecs, tvecs
def cal_undistort(img, objpoints, imgpoints, mtx, dist):
''' Function that takes an image, object points, and image points
performs the camera calibration, image distortion correction
and returns the undistorted image '''
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(20, 100)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
grad_binary = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
grad_binary.astype(int)
# Return the result
return grad_binary
def mag_thresh(img, sobel_kernel=3, thresh=(20, 100)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
mag_binary = np.zeros_like(gradmag)
mag_binary[(gradmag >= thresh[0]) & (gradmag <= thresh[1])] = 1
mag_binary.astype(int)
# Return the binary image
return mag_binary
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
dir_binary = np.zeros_like(absgraddir)
dir_binary[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
dir_binary.astype(int)
# Return the binary image
return dir_binary
def color_threshold(img, s_thresh=(0,255), v_thresh=(0,255)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1]) ] = 1
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
v_channel = hsv[:,:,2]
v_binary = np.zeros_like(v_channel)
v_binary[(v_channel >= v_thresh[0]) & (v_channel <= v_thresh[1]) ] = 1
color_binary = np.zeros_like(s_channel)
color_binary[(s_binary == 1) & (v_binary == 1)] = 1
return color_binary
def warper(img, src, dst):
# Compute and apply perpective transform
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image
return warped
In [4]:
'''---Camera Calibration---------------------------------------------------------------------------------------------------'''
# Read in a image, find the CheesboardCorners and save the image with the corners
objpoints=[]
imgpoints=[]
objp = np.zeros((9*6,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1, 2)
for filepath in glob.glob('camera_cal/*.jpg'):
img = mpimg.imread(filepath)
file=filepath.split('\\')
filename=file[1]
#cv2.imshow('img_orig',img)
#cv2.waitKey(500)
# Convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#cv2.imshow('img_gray',gray)
#cv2.waitKey(500)
# Find the CheesboardCorners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
print(ret)
# CheesboardCorners found, add object points and image points, save the image with the corners
# run the image distortion correction and save the undistorted images
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
mtx = cam_cali(objpoints, imgpoints)[1]
dist = cam_cali(objpoints, imgpoints)[2]
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
#cv2.imshow('img_corners',img)
#cv2.waitKey(500)
#print (target+filename)
mpimg.imsave(target_corners+filename,img)
undistorted = cal_undistort(img, objpoints, imgpoints, mtx, dist)
#cv2.imshow('img_undist',undistorted)
#cv2.waitKey(500)
mpimg.imsave(target_undist+filename,undistorted)
cv2.destroyAllWindows()
In [5]:
'''Perspective Transform of the Straight Line Test Data for Output Images'''
for filepath in glob.glob('output_images/undistorted/*straight_lines*.jpg'):
img = mpimg.imread(filepath)
file=filepath.split('\\')
filename=file[1]
src=np.float32([[753,480],
[537,480],
[190,700],
[1150,700]])
dst=np.float32([[1140,100],
[200,100],
[200,740],
[1140,740]])
binary_warped=warper(img, src, dst)
#Plot the src points on the original image
plt.figure()
plt.imshow(img)
plt.plot(537,480,'+',color='b')#top left
plt.plot(753,480,'*',color='b')#top right
plt.plot(190,700,'x',color='b') #bottom left
plt.plot(1150,700,'o',color='b')#bottom right
#Plot the dst points on the warped image
plt.figure()
plt.imshow(binary_warped)
plt.plot(200,100,'+',color='r')#top left
plt.plot(1140,100,'*',color='r')#top right
plt.plot(200,650,'x',color='r') #bottom left
plt.plot(1140,650,'o',color='r')#bottom right
#Plot the distorted rectangle on the original image
pts = np.array(src, np.int32)
pts = pts.reshape((-1,1,2))
cv2.polylines(img,[pts],True,1000,5)
#Plot the undistorted rectangle on the warped image
pts = np.array(dst, np.int32)
pts = pts.reshape((-1,1,2))
cv2.polylines(binary_warped,[pts],True,(255,0,255),5)
f1, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f1.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(binary_warped)
ax2.set_title('Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
'Save image for the output folder'
f1.savefig(target_perspective+filename)
In [8]:
'''Pipeline with Test Images'''
# Read the test images
for filepath in glob.glob('test_images/*.jpg'):
img = mpimg.imread(filepath)
file=filepath.split('\\')
filename=file[1]
'''Run the image distortion correction and save the undistorted images'''
undistorted = cal_undistort(img, objpoints, imgpoints, mtx, dist)
'Save image for the output folder'
mpimg.imsave(target_undist+filename,undistorted)
# Plot example results
#f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
#f.tight_layout()
#ax1.imshow(img)
#ax1.set_title('Original Image', fontsize=50)
#ax2.imshow(undistorted)
#ax2.set_title('Undistorted Image', fontsize=50)
#plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
#plt.show()
'''Call the pipeline to preprocess the undistorted images and save die preprocessed images'''
result_comb = np.zeros_like(img[:,:,0])
result_color = color_threshold(img, s_thresh=(100,255), v_thresh=(200,255))
result_gradx = abs_sobel_thresh(img, orient='x', thresh=(25,255))
result_grady = abs_sobel_thresh(img, orient='y', thresh=(25,255))
result_comb[((result_gradx == 1) & (result_grady == 1) | (result_color == 1) )] = 255
'Save image for the output folder'
mpimg.imsave(target_pipeline_comb+filename,result_comb, cmap='gray')
'''Call the warper function to get a binary warped image'''
src=np.float32([[753,480],
[537,480],
[190,700],
[1150,700]])
dst=np.float32([[1140,100],
[200,100],
[200,740],
[1140,740]])
binary_warped=warper(result_comb, src, dst)
# Calculate the Transformation Matrix and the inverse Transformation Matrix
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst,src)
'Save image for the output folder'
mpimg.imsave(target_warped+filename,binary_warped, cmap='gray')
#Plot example results
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(result_comb, cmap='gray')
ax1.set_title('Processed Image', fontsize=50)
ax2.imshow(binary_warped, cmap='gray')
ax2.set_title('Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
'''Finding the lanes on the binary warped image - Udacity Suggestion'''
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 5)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 5)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.figure()
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.figure()
'''Calculate the Radius of Curvature'''
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
print('Left Curvature',"%.2f" % left_curverad, 'm')
print('Right Curvature', "%.2f" % right_curverad, 'm')
#Calculate the vehicle offset from the center
middle_fitx=(right_fitx[719]-left_fitx[719])/2 + left_fitx[719]
vehicle_offset=((binary_warped.shape[1]/2)-middle_fitx)*xm_per_pix
#print('Vehicle Offset from the Center of Road', "%.2f" % vehicle_offset, 'm')
print('Vehicle Offset from the Middle', "%.2f" % vehicle_offset, 'm')
#Calculate the average radius of curvature
avg_curverad = (left_curverad + right_curverad)/2
print('Average Curvature', "%.2f" % avg_curverad, 'm')
'''Create an image to draw the line position on'''
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Draw lane markers
pts = np.transpose(np.vstack([left_fitx, ploty])).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (255,0,0), thickness=30)
pts = np.transpose(np.vstack([right_fitx, ploty])).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (0,0,255), thickness=30)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistorted, 1, newwarp, 0.4, 0)
# Draw the text showing curvature, offset
cv2.putText(result,'Radius of Curvature = '+str(round(avg_curverad,0))+'m',(50,50) , cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 255, 255),3)
cv2.putText(result,'Vehicle Offset from Center = '+str(round(vehicle_offset,2))+'m',(50,100) , cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 255, 255),3)
plt.imshow(result)
cv2.destroyAllWindows()
In [ ]:
In [7]:
'''Pipeline with project video'''
from moviepy.editor import VideoFileClip
import numpy as np
import cv2
import glob
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
from os import path
def cam_cali(objpoints, imgpoints):
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
return ret, mtx, dist, rvecs, tvecs
def cal_undistort(img, objpoints, imgpoints, mtx, dist):
''' Function that takes an image, object points, and image points
performs the camera calibration, image distortion correction
and returns the undistorted image '''
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(20, 100)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
grad_binary = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
grad_binary.astype(int)
# Return the result
return grad_binary
def color_threshold(img, s_thresh=(0,255), v_thresh=(0,255)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1]) ] = 1
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
v_channel = hsv[:,:,2]
v_binary = np.zeros_like(v_channel)
v_binary[(v_channel >= v_thresh[0]) & (v_channel <= v_thresh[1]) ] = 1
color_binary = np.zeros_like(s_channel)
color_binary[(s_binary == 1) & (v_binary == 1)] = 1
return color_binary
def warper(img, src, dst):
# Compute and apply perpective transform
img_size = (img.shape[1], img.shape[0])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image
return warped
def process_image(img):
'''Run the image distortion correction and save the undistorted images'''
undistorted = cal_undistort(img, objpoints, imgpoints, mtx, dist)
'''Call the pipeline to preprocess the undistorted images and save die preprocessed images'''
#result = pipeline_hsv(undistorted, s_thresh=(170, 255), sx_thresh=(50, 255))
result_comb = np.zeros_like(img[:,:,0])
result_color = color_threshold(img, s_thresh=(100,255), v_thresh=(200,255))
result_gradx = abs_sobel_thresh(img, orient='x', thresh=(25,255))
result_grady = abs_sobel_thresh(img, orient='y', thresh=(25,255))
result_comb[((result_gradx == 1) & (result_grady == 1) | (result_color == 1) )] = 255
'''Call the warper function to get a binary warped image'''
src=np.float32([[753,480],
[537,480],
[190,700],
[1150,700]])
dst=np.float32([[1140,100],
[200,100],
[200,740],
[1140,740]])
binary_warped=warper(result_comb, src, dst)
# Calculate the Transformation Matrix and the inverse Transformation Matrix
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst,src)
'''Finding the lanes on the binary warped image - Udacity Suggestion'''
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 5)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 5)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
'''Calculate the Radius of Curvature'''
# Define y-value where we want radius of curvature
# I'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
#Calculate the vehicle offset from the center
middle_fitx=(right_fitx[719]-left_fitx[719])/2 + left_fitx[719]
vehicle_offset=((binary_warped.shape[1]/2)-middle_fitx)*xm_per_pix
#Calculate the average radius of curvature
avg_curverad = (left_curverad + right_curverad)/2
'''Create an image to draw the line position on'''
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Draw lane markers
pts = np.transpose(np.vstack([left_fitx, ploty])).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (255,0,0), thickness=30)
pts = np.transpose(np.vstack([right_fitx, ploty])).reshape((-1,1,2)).astype(np.int32)
cv2.drawContours(color_warp, pts, -1, (0,0,255), thickness=30)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undistorted, 1, newwarp, 0.4, 0)
# Draw the text showing curvature, offset
cv2.putText(result,'Radius of Curvature = '+str(round((avg_curverad/1000),2))+'km',(50,50) , cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 255, 255),3)
cv2.putText(result,'Vehicle Offset from Center = '+str(round(vehicle_offset,2))+'m',(50,100) , cv2.FONT_HERSHEY_SIMPLEX, 1,(255, 255, 255),3)
return result
clip1 = VideoFileClip("project_video.mp4")
video_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
video_clip.write_videofile("output_video.mp4", audio=False)
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